System and method for change analysis

ABSTRACT

In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/290,174 filed 16 Dec. 2021, and U.S. Provisional Application No.63/350,124 filed 8 Jun. 2022, each of which is incorporated in itsentirety by this reference.

TECHNICAL FIELD

This invention relates generally to the property change field, and morespecifically to a new and useful system and method in the propertychange field.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a flowchart representation of a variant of the method.

FIG. 1B is a schematic representation of a variant of the system.

FIG. 2 is a first flowchart representation of a variant of the method.

FIG. 3A is a first illustrative example of training a representationmodel.

FIG. 3B is a first illustrative example of change detection using therepresentation model.

FIG. 4A is a second illustrative example of training a representationmodel.

FIG. 4B is a second illustrative example of change detection using therepresentation model.

FIG. 4C is a third illustrative example of training a representationmodel.

FIG. 4D is a third illustrative example of change detection using therepresentation model.

FIG. 5A is a fourth illustrative example of training a representationmodel.

FIG. 5B is a fourth illustrative example of change detection using therepresentation model.

FIG. 6 is a second flowchart representation of a variant of the method.

FIG. 7 is a first illustrative example of training a changeclassification model.

FIG. 8 is a second illustrative example of training a changeclassification model.

FIG. 9 is a third illustrative example of training a changeclassification model.

FIG. 10 is an illustrative example of determining a time change.

FIG. 11 is an illustrative example of determining an image segment for ageographic region.

FIG. 12 is a fifth illustrative example of training a representationmodel.

FIG. 13 is an illustrative example of determining that no geographicregion change occurred.

FIG. 14 is an illustrative example of determining that a geographicregion change has occurred.

FIG. 15 is an illustrative example of determining a change type.

FIG. 16A is a fourth illustrative example of training a changeclassification model.

FIG. 16B is a fifth illustrative example of training a changeclassification model.

FIG. 17A is a sixth illustrative example of training a changeclassification model.

FIG. 17B is a seventh illustrative example of training a changeclassification model.

FIG. 18 is a first illustrative example of determining a changedescription.

FIG. 19A is a second illustrative example of determining a changedescription.

FIG. 19B is a third illustrative example of determining a changedescription.

FIG. 20 is an illustrative example of determining a aggregate geographicregion representation.

FIG. 21 is an illustrative example of determining a geographic regiontypicality.

DETAILED DESCRIPTION

The following description of the embodiments of the invention is notintended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

1. Overview

As shown in FIG. 1A, the method for change analysis can include:training a representation model S100 and evaluating a geographic regionS300. However, the method can additionally or alternatively include anyother suitable elements.

The method functions to detect rare changes (e.g., caused by disaster,house fire, remodeling, etc.) for a given geographic region (e.g.,property, address, geocoordinates, point of interest, geographiclocation, etc.). In variants, the method can additionally oralternatively generate a common-change-agnostic model (e.g., wherein themethod trains a model to be agnostic to common changes, such that rarechanges can be more accurately detected), generate a baselinecommon-change-agnostic representation for a geographic region (e.g.,baseline appearance-based representation for the geographic region),train a change classification model, identify one or more geographicregions associated with a change type and/or any other changeinformation, and/or perform other functionalities.

2. Examples

In an example, the method can include: determining a baseline vectorrepresentative of a geographic region based on a baseline image of saidgeographic region from a first timestamp using a common-change-agnosticmodel; determining a test vector of the geographic region based on atest image of said geographic region from a different timestamp usingthe common-change-agnostic model; detecting a rare change when the testvector differs from the baseline vector (e.g., differs beyond athreshold value); and optionally classifying the rare change using achange classification model. In this example, the common-change-agnosticmodel can be trained (e.g., using self-supervised learning) based on aset of training images, wherein the common-change-agnostic model istrained to output the same vector for each image of the same geographicregion. The set of training images can include a set of heterogeneousimages of each geographic region. For example, the set of training caninclude images depicting common changes (e.g., depicting the same,substantially unchanged geographic region under different visualconditions, such as different lighting, shadows, obstructions, etc.).The set of training images can optionally include and/or exclude imagesof the geographic region depicting rare changes (e.g., depictingproperty damage, property construction, etc.). However, rare changes toa given geographic region can be otherwise detected.

3. Technical Advantages

Variants of the technology can confer one or more advantages overconventional technologies.

First, the technology can detect rare changes (e.g., caused by disaster,house fire, remodeling, etc.) to a geographic region while beingagnostic to common changes (e.g., caused by shadows, tree occlusions,car presence, seasons, planes, clouds, different measurement providers,different sensing modalities, etc.). The inventors have discovered thata comprehensive common change taxonomy is difficult to build, whichmakes it difficult to train machine learning models to comprehensivelydetect or ignore every individual common change. This technology enablesa common-change-agnostic model to be trained, such that rare changes canbe more easily detected. For example, this technology can train arepresentation model to output the substantially same representation(e.g., the same feature vector) from different images—depictingdifferent common changes—of the same geographic region (examples shownin FIG. 12 and FIG. 13 ), such that a rare change event can be detectedwhen a different representation is output for said geographic region(example shown in FIG. 14 ).

Second, the technology can determine the type of change (e.g., changetype) and when the change occurred (e.g., change time) at the geographicregion. For example, the technology can use a search method (e.g.,binary search) through the timeseries of images for a given geographicregion to identify when the change occurred.

Third, variants of the technology can enable machine learning models tobe trained using representation learning and/or self-supervisedlearning. This can overcome the challenges of sparse data (e.g., sincemeasurements of the geographic region may be taken infrequently) withlimited or no availability of ground-truth labels.

Fourth, variants of the technology can leverage visual features (e.g.,appearance-based features) instead of geometric features when evaluatingwhether a change has occurred. The inventors have discovered that, insome situations, geometric features extracted from remote measurements(e.g., remote imagery) using appearance-based methods can be highlysusceptible to common changes and remote measurement errors (e.g.,during geo-registration process), and that, surprisingly, visualfeatures can be more robust to said common changes and remotemeasurement errors. This can be particularly useful when comparingmeasurements from different modalities (e.g., aerial and satelliteimagery of the same property), which oftentimes depict common changes(e.g., due to georegistration discrepancy, atmospheric effects,misalignment, etc.) even if sampled contemporaneously. However,geometric features can additionally or alternatively be used (e.g., thecommon-change-agnostic model can be trained to extract the samegeometric feature vector given different geometric measurements for agiven geographic region).

Fifth, variants of the technology can store the representations insteadof the measurements, which can minimize the amount of storage necessaryto store a timeseries of data for a given geographic region.

Sixth, variants of the technology can increase the computationalefficiency of evaluating a large dataset of geographic regions (e.g.,identifying and classifying rare changes for each geographic region,identifying one or more geographic regions in the dataset thatexperienced a given rare change type, etc.). In an example, the datasetof geographic regions can be filtered to identify geographic regionsthat have experienced a rare change, such that a downstream changeclassification can be performed for the filtered subset of geographicregions instead of the unfiltered dataset.

However, further advantages can be provided by the system and methoddisclosed herein.

4. System

As shown in FIG. 1B, the system can include one or more representationmodels, change classification models, and/or any other set of models.The system can optionally include a computing system, a database, and/orany other suitable components. In variants, the system can function todetect rare changes for a given geographic region, to train one or moremodels (e.g., a change-agnostic model), generate a baseline,common-change-agnostic representation for a geographic region, toidentify one or more geographic regions, store geographic regioninformation, receive requests, and/or perform other functionalities.

The system can be used with one or more geographic regions. Thegeographic regions can function as test geographic regions (e.g., ageographic region of interest), training geographic regions (e.g., usedto train one or more models), and/or be otherwise used.

Each geographic region can be or include: a property, a point ofinterest, a land (e.g., a parcel, land region, etc.), a region boundary(e.g., property parcel, neighborhood, zip code, census block group,city, state, etc.), a landmark, a geographic region component or set orsegment thereof, and/or otherwise defined. A property can: include boththe underlying land and improvements (e.g., built structures, fixtures,etc.) affixed to the land, only include the underlying land, or onlyinclude a subset of the improvements (e.g., only the primary building).Geographic region components can include: built structures (e.g.,primary structure, accessory structure, deck, pool, etc.); subcomponentsof the built structures (e.g., roof, siding, framing, flooring, livingspace, bedrooms, bathrooms, garages, foundation, HVAC systems, solarpanels, slides, diving board, etc.); permanent improvements (e.g.,pavement, statutes, fences, etc.); temporary improvements or objects(e.g., trampoline); vegetation (e.g., tree, flammable vegetation, lawn,etc.); land subregions (e.g., driveway, sidewalk, lawn, backyard, frontyard, wildland, etc.); debris; and/or any other suitable component. Thegeographic regions and/or components thereof are preferably physical,but can alternatively be virtual.

Each geographic region can be identified by one or more geographicregion identifiers. A geographic region identifier (geographic regionID) can include: geographic coordinates, geocode, an address, a parcelidentifier, property identifier, a block/lot identifier, a planningapplication identifier, a municipal identifier (e.g., determined basedon the ZIP, ZIP+4, city, state, etc.), and/or any other identifier. Thegeographic region identifier can be used to retrieve geographic regioninformation, such as parcel information (e.g., parcel boundary, parcellocation, parcel area, etc.), geographic region measurements, geographicregion descriptions, and/or other geographic region data. The geographicregion identifier can additionally or alternatively be used to identifya geographic region component, such as a primary building or secondarybuilding, and/or be otherwise used.

Each geographic region can be associated with geographic regioninformation. The geographic region information can be static (e.g.,remain constant over a threshold period of time) or variable (e.g., varyover time). The geographic region information can be associated with: atime (e.g., a generation time, a valid duration, etc.), a source (e.g.,the information source), an accuracy or error, and/or any other suitablemetadata. The geographic region information is preferably specific tothe geographic region, but can additionally or alternatively be fromother geographic regions (e.g., neighboring geographic regions, othergeographic regions sharing one or more attributes with the geographicregion). Examples of geographic region information can include:measurements, descriptions, representations (e.g., baselinerepresentation, appearance feature vectors, etc.), attributes, auxiliarydata, and/or any other suitable information about the geographic region.

Geographic region measurements preferably measure an aspect about thegeographic region, such as a visual appearance, geometry, and/or otheraspect. In variants, when the geographic region includes a property, thegeographic region measurements can depict the property (e.g., theproperty of interest), but can additionally or alternatively depict thesurrounding region, adjacent properties, and/or other factors. Themeasurement can be: 2D, 3D, and/or have any other set of dimensions.Examples of measurements can include: images, surface models (e.g.,digital surface models (DSM), digital elevation models (DEM), digitalterrain models (DTM), etc.), polygons, point clouds (e.g., generatedfrom LIDAR, RADAR, stereoscopic imagery, etc.), depth maps, depthimages, virtual models (e.g., geometric models, mesh models), audio,video, radar measurements, ultrasound measurements, and/or any othersuitable measurement. Examples of images that can be used include: RGBimages, hyperspectral images, multispectral images, black and whiteimages, grayscale images, panchromatic images, IR images, NIR images, UVimages, NDVI images, thermal images, and/or images sampled using anyother set of wavelengths; images with depth values associated with oneor more pixels (e.g., DSM, DEM, etc.); and/or other images.

The measurements can include: remote measurements (e.g., aerial imagery,satellite imagery, balloon imagery, drone imagery, radar, sonar, LightDetection and Ranging (LIDAR), seismography, etc.), local or on-sitemeasurements (e.g., sampled by a user, streetside measurements, etc.),and/or sampled at any other proximity to the geographic region. Theremote measurements can be measurements sampled more than a thresholddistance away from the geographic region (e.g., from a geographic regioncomponent), such as more than 100 ft, 500 ft, 1,000 ft, any rangetherein, and/or sampled any other distance away from the geographicregion. The measurements can be: top-down measurements (e.g., nadirmeasurements, panoptic measurements, etc.), side measurements (e.g.,elevation views, street measurements, etc.), angled and/or obliquemeasurements (e.g., at an angle to vertical, orthographic measurements,isometric views, etc.), and/or sampled from any other pose or anglerelative to the geographic region. The measurements can depict ageographic region exterior, a geographic region interior, and/or anyother view of the geographic region.

The measurements can be a full-frame measurement, a segment of themeasurement (e.g., the segment depicting the geographic region, such asthat depicting the geographic region's parcel; the segment depicting ageographic region a predetermined distance away from the geographicregion; etc.), a merged measurement (e.g., a mosaic of multiplemeasurements), orthorectified, and/or otherwise processed. Themeasurements can include tiles (e.g., of geographic regions), chips(e.g., of a built structure), parcel segments (e.g., of a propertyparcel), and/or any other suitable segments. The property can optionallybe associated with a parcel (e.g., property parcel), wherein the parcelcan be used to identify the segment of a larger-scale measurementdepicting the property (example shown in FIG. 11 ), or otherwise used.

The measurements can be sampled (e.g., measured, acquired, etc.) at ameasurement time. The measurements can be received as part of a userrequest, retrieved from a database, determined using other data (e.g.,segmented from an image, generated from a set of images, etc.),synthetically determined, and/or otherwise determined. The measurementscan be from the same measurement provider (e.g., vendor) or fromdifferent measurement providers (e.g., retrieved from different providerdatabases).

The geographic region information can include geographic regiondescriptions. The geographic region description can be: a writtendescription (e.g., a text description), an audio description, and/or inany other suitable format. The geographic region description ispreferably verbal but can alternatively be nonverbal. Examples ofgeographic region descriptions can include: listing descriptions (e.g.,from a realtor, listing agent, etc.), property disclosures, inspectionreports, permit data, change descriptions, appraisal reports, and/or anyother text based description of a geographic region.

The geographic region information can include auxiliary data. Examplesof auxiliary data can include the geographic region descriptions, permitdata, insurance loss data, inspection data, appraisal data, broker priceopinion data, valuations, geographic region attribute and/or componentdata (e.g., values), historical weather and/or hazard data, measurementcontext (e.g., measurement acquisition pose, measurement provider,measurement time, sensing modality, obstructions, etc.), and/or anyother suitable data.

However, the geographic region information can include any othersuitable information about the geographic region.

Each geographic region can optionally be associated with a set ofgeographic region attributes, which function to represent one or moreaspects of a given geographic region. The geographic region attributescan be semantic, quantitative, qualitative, and/or otherwise describethe geographic region. Each geographic region can be associated with itsown set of geographic region attributes, and/or share geographic regionattributes with other geographic regions. As used herein, geographicregion attributes can refer to the attribute parameter (e.g., thevariable) and/or the attribute value (e.g., value bound to the variablefor the geographic region).

Geographic region attributes can include: geographic region components,features (e.g., feature vector, mesh, mask, point cloud, pixels, voxels,any other parameter extracted from a measurement), any parameterassociated with a geographic region component (e.g., geographic regioncomponent characteristics), semantic features (e.g., whether a semanticconcept appears within the geographic region information), and/orhigher-level summary data extracted from geographic region componentsand/or features. Geographic region attributes can be determined based ongeographic region information for the geographic region itself,neighboring properties, and/or any other set of properties. Geographicregion attributes can be automatically determined (e.g., using a model),manually determined, and/or otherwise determined.

Geographic region attributes can include: structural attributes,condition attributes, record attributes, semantic attributes, subjectiveattributes, and/or any other suitable set of attributes. In a specificexample, when the geographic region is a property, the geographic regionattributes for the property can include a property class. Propertyclasses can include a residential property (e.g., single-family house,multi-family house, apartment building, condo, etc.), a commercialproperty (e.g., industrial center, forest land, farmland, quarry, etc.),and/or any other suitable property class. Examples of geographic regionattributes can include: location, structure size, structure footprint,roof geometry (e.g., slope, facets, etc.), roof material, yard debris,vegetation coverage, and/or any other suitable attribute.

In examples, geographic region attributes and/or values thereof candefined and/or determined as disclosed in U.S. application Ser. No.17/529,836 filed on 18 Nov. 2021, U.S. application Ser. No. 17/475,523filed 15 Sep. 2021, U.S. application Ser. No. 17/749,385 filed 20 May2022, U.S. application Ser. No. 17/870,279 filed 21 Jul. 2022, and/orU.S. application Ser. No. 17/858,422 filed 6 Jul. 2022, each of which isincorporated in its entirety by this reference (e.g., wherein featuresand/or feature values disclosed in the references can correspond toattributes and/or attribute values).

Each geographic region can optionally be associated with one or morerepresentations (e.g., geographic region representations). Therepresentation is preferably representative of a visual appearance ofthe geographic region, but can alternatively be representative of thegeometry of the geographic region, or represent any other set ofattributes and/or features of the geographic region. The representationis preferably a vector (e.g., feature vector), but can additionally oralternatively be an array, a set, a matrix, a map, an embedding, anencoding, a multidimensional surface, a single value, and/or any othersuitable representation. The representation can be any shape (e.g., ofany dimension). The representation preferably includes values fornonsemantic features, but can additionally or alternatively includevalues for semantic features (e.g., attribute values) and/or any otherinformation. The representation is preferably extracted using arepresentation model, but can additionally or alternatively bedetermined by the user, retrieved from a database, and/or otherwisedetermined.

A geographic region can experience one or more common changes, rarechanges, and/or no changes.

Common changes are preferably temporary and/or transient changes to theappearance of the geographic region (e.g., last less than a day, lastless than a year, are not structural changes to or within the geographicregion, etc.), but can alternatively be permanent. Common changes (e.g.,common differences) can be caused by the environment (e.g., shadows,tree occlusions, seasons, clouds, birds, light glare, weather, sunangle, etc.), be manmade (e.g., car and/or other vehicle presence,planes, repainting, decorations, temporary structures, etc.), be causedby obstructions, be due to the data source (e.g., different measuringentities, different sensing modalities, different post-processing steps,different measurement acquisition poses relative to the geographicregion, other differing measurement contexts, etc.), and/or otherwisecreated. Obstructions can include environmental obstructions (e.g.,clouds, shadows, trees, tree leaves, bushes, other plants, light glare,etc.), manmade obstructions (e.g., car, planes, other vehicles,decorations, temporary structures, etc.), orthorectification errors,and/or any other visual obstructions of the geographic region. Temporarystructures can include tents, event structures, portable/mobile toilets,trampolines, playsets, and/or any other temporary and/or mobilestructures. Measurement acquisition pose can include measurementacquisition angle relative to the geographic region, position relativeto the geographic region (e.g., location, distance from the geographicregion, etc.), and/or any other geometric relationship. Common changescan be changes (e.g., appearance changes, geometric changes, measurementchanges) that occur in: more than a threshold proportion of themeasurements (e.g., more than 30%, 50%, 60%, 70%, 80%. 90% 95%, 99%,etc. of the measurements), more than a threshold proportion of thegeographic regions (e.g., more than 30%, 50%, 60%, 70%, 80%. 90% 95%,99%, etc. of the geographic), and/or at more than a threshold frequency(e.g., more frequently than once a day, week, month, year, 2 years, 5years, 10 years, etc.), and/or be otherwise defined.

Rare changes are preferably permanent changes (e.g., structural changes,long-term changes, semi-permanent changes, etc.), but can alternativelybe temporary and/or transient. Rare changes preferably occur for lessthan a threshold percentage of geographic regions in a given time window(e.g., wherein the time window can be 1 day, 1 week, 1 month, 1 year, 2years, 5 years, 10 years, etc.), but can alternatively occur with anyother frequency. The threshold percentage of geographic regions (e.g.,properties) can be between 0.001%-30% or any range or value therebetween(e.g., 0.005%, 0.01%, 0.05%, 0.1%, 0.5%, 1%, 2%, 5%, 10%, 15%, 20%, 30%,etc.), but can alternatively be less than 0.001% or greater than 30%.For a dataset including pairs of measurements (each pair correspondingto a geographic region), rare changes are preferably depicted in lessthan a threshold percentage of the measurement pairs, but alternativelycan be depicted at any frequency. The threshold percentage ofmeasurement pairs can be between 0.001%-30% or any range or valuetherebetween (e.g., 0.005%, 0.01%, 0.05%, 0.1%, 0.5%, 1%, 2%, 5%, 10%,15%, 20%, 30%, etc.), but can alternatively be less than 0.001% orgreater than 30%.

Rare changes can be caused by a disaster and/or weather (e.g., hail,wildfire, tornadoes, storm, hurricane, earthquake, flooding, drought,etc.), be man-made (e.g., demolition, construction, renovation, etc.),and/or be otherwise caused. A disaster is preferably an environmentaldisaster (e.g., earthquake, hurricane, etc.) and/or widespread disaster(e.g., that encompass more than one geographic region), but canadditionally or alternatively be a manmade disaster (e.g., a housefire), property-specific disaster (e.g., a house fire), and/or any othersuitable disaster. Rare changes can include a property damage event(e.g., house fire, house flood, robbery, vandalization, etc.), propertyloss event, property construction event (e.g., remodeling, roof repair,auxiliary structure addition and/or removal, built structure footprintchange, etc.), property demolition event, and/or any other suitable rarechange.

A change (e.g., rare change and/or common change) can optionally beassociated with change information. Examples of change informationelements include: a change type (e.g., change classification), changeextent, change magnitude, change time (e.g., a time when the changeevent occurred, a time range during which the change event occurred,etc.), change cost (e.g., cost to repair a property damage event),change occurrence (e.g., a probability that a given geographic regionexperienced the change event), change cause, change description (e.g.,one or more longform text descriptions and/or other text labelscorresponding to the change type), change uncertainty, any other changecharacteristic, and/or any other change information.

Change types can include a natural change type (e.g., related to achange in a tree, a body of water, vegetation, etc.), an artificialchange type (e.g., related to a change in a house, a building, a roof, apool, a driveway, a shed, etc.), any/or any other suitable change typeclassification.

Change information can be numerical (e.g., 0 vs. 1),categorical/multiclass (e.g., low change, medium change, high change),binary (e.g., change has occurred vs. change has not occurred),non-binary (e.g., 1, 2, 3, 4, 5), discrete, continuous, and/or otherwisestructured.

Models in the system (e.g., the representation model, the changeclassification model, etc.) can be or use one or more of: regression(e.g., leverage regression), classification (e.g., binary classifiers,multiclass classifiers, semantic segmentation models, instance-basedsegmentation models, etc.), neural networks (e.g., CNNs, DNNs, encoders,etc.), rules, heuristics (e.g., inferring the number of stories of aproperty based on the height of a property), equations (e.g., weightedequations, etc.), selection and/or retrieval (e.g., from a databaseand/or library), instance-based methods (e.g., nearest neighbor),regularization methods (e.g., ridge regression), decision trees,Bayesian methods (e.g., Naïve Bayes, Markov, etc.), kernel methods,deterministics, genetic programs, support vectors, optimization methods,statistical methods (e.g., probability), comparison methods (e.g.,vector comparison, image comparison, matching, distance metrics,thresholds, etc.), clustering methods (e.g., k-means clustering),principal component analysis, local linear embedding, independentcomponent analysis, unrestricted Boltzmann machines, encoders (e.g.,autoencoders, variational autoencoders, regularized autoencoder,concrete autoencoder, etc.), contrastive language-image pre-training(CLIP) models, vision transformers, segmentation algorithms (e.g.,neural networks, such as CNN based algorithms, thresholding algorithms,clustering algorithms, etc.), isolation forests, robust random cutforest, object detectors (e.g., CNN based algorithms, such asRegion-CNN, fast RCNN, faster R-CNN, YOLO, SSD-Single Shot MultiBoxDetector, R-FCN, etc.; feed forward networks, transformer networks,and/or other neural network algorithms), key point extraction, SIFT, anycomputer vision and/or machine learning method (e.g., CV/ML extractionmethods), and/or any other suitable method. The models can be trained,learned, fit, predetermined, and/or can be otherwise determined. Themodels can be trained using self-supervised learning, semi-supervisedlearning, supervised learning, unsupervised learning, transfer learning,reinforcement learning, single-shot learning, zero-shot learning, and/orany other suitable training method.

The representation model (e.g., geographic region representation model)can function to extract a representation from a measurement of thegeographic region. Inputs to the representation model can include one ormore measurements for a geographic region, other geographic regioninformation, auxiliary data (e.g., measurement context data), and/or anyother suitable inputs. Outputs from the representation model can includeone or more representations for the geographic region and/or any othersuitable outputs. In a first example, the representation model receivesone measurement and outputs one representation. In a second example, therepresentation model receives multiple measurements (e.g., twomeasurements) and outputs one representation (e.g., a commonrepresentation). However, the representation model can receive anynumber of measurements and/or output any number of representations.However, the representation model can be otherwise configured. Therepresentation model is preferably trained using a feature learningmethod (e.g., supervised learning, unsupervised learning, etc.), but canbe otherwise trained. The representation model is preferably a deeplearning model, but can alternatively be another model. In examples, therepresentation model can be an autoencoder, an encoder, a decoder, aBoltzmann machine, and/or be any other suitable model.

The optional change classification model can function to classify thetype of change (e.g., the type of rare change) depicted acrosspre-change and post-change measurements (example shown in FIG. 15 ).Inputs to the change classification model can include geographic regionrepresentations (e.g., a pair of representations for a pair ofmeasurements, a single measurement-pair representation for a pair ofmeasurements, etc.), one or more measurements, a comparison metric,change information (e.g., change type, change description, etc.),auxiliary data (e.g., measurement context data), outputs from therepresentation model, and/or any other suitable inputs. Examples ofmeasurement inputs include: a measurement depicting the geographicregion before and/or after a change event, a set (e.g., pair) ofmeasurements spanning the change (e.g., a series of images), and/or anyother measurements. Inputs can be determined via user input (e.g., auser specified change type and/or change description of interest, a userspecified geographic region, a user specified measurement time, etc.),selected from a database (e.g., select measurements and/orrepresentations corresponding to a geographic region of interest and/ora time of interest), automatically determined (e.g., using therepresentation model), and/or otherwise determined. Outputs from thechange classification model can include a change type (e.g., changeclassification) and/or change description, a probability (e.g.,likelihood) of one or more inputs (e.g., a pair of measurements, a pairof representations, etc.) corresponding to a given change type and/orchange description, any other change information, and/or any othersuitable outputs. The change classification model can optionally be orinclude the representation model (e.g., the change classification modelis a downstream layer of the representation model), interface with therepresentation model (e.g., in series, in parallel, etc.), and/or beunrelated to the representation model. However, the changeclassification model can be otherwise configured.

The system can optionally include a database which can function to storegeographic region identifiers, geographic region information (e.g.,location, measurements, representations, change information, etc.),and/or any other information. The database can be local, remote,distributed, or otherwise arranged relative to any other system ormodule. In variants, the database can be or interface with a third-partysource (e.g., third-party database, MLS database, city permittingdatabase, historical weather and/or hazard database, tax assessordatabase, CONUS data, etc.), but can alternatively not interface with athird-party source. For example, information in the database can beretrieved, linked, or otherwise associated with information in athird-party source. The database can optionally be queried (e.g., basedon a geographic region identifier, based on change information, etc.) toretrieve measurements, geographic regions, change information, and/orany other information in the database.

The system can optionally include a computing system. The computingsystem can function to execute all or portions of the method, and/orperform any other suitable functionality. The computing system can belocal (e.g., a user device such as a smartphone, laptop, desktop,tablet, etc.), remote (e.g., one or more servers, one or more platforms,etc.), distributed, or otherwise arranged relative to any other systemor module. The computing system can include one or more: CPUs, GPUs,custom FPGA/ASICS, microprocessors, servers, cloud computing, and/or anyother suitable components. The computing system can be used with a userinterface (e.g., mobile application, web application, desktopapplication, API, database, etc.) or not be used with a user interface.The user interface can optionally be used to receive and/or input:geographic region identifiers, change information (e.g., changeclassifications, change descriptions, etc.), and/or any other inputs.The user interface can optionally be used to provide and/or output:geographic region identifiers, change information and/or any otheroutputs. The computing system can optionally interface with thedatabase(s).

5. Method

As shown in FIG. 1A, the method can include: training a representationmodel S100 and evaluating a geographic region S300, but can additionallyor alternatively include any other suitable elements. The method canoptionally include: training a change classification model S200,determining a geographic region satisfying a change criterion S400,and/or any other suitable elements.

All or portions of the method can be performed by one or more componentsof the system, by a user, and/or by any other suitable system. All orportions of the method can be performed automatically, manually,semi-automatically, and/or otherwise performed.

All or portions of the method can be performed in real time, in responseto a request from an endpoint, before receipt of a request, iteratively,asynchronously, periodically, and/or at any other suitable time. All orportions of the method can be performed for one geographic region,multiple geographic regions, and/or any other suitable number ofgeographic regions. All or portions of the method can be performed forall geographic regions within a geographic region set (e.g., allgeographic regions appearing in a map, within a larger region, within alarge-scale measurement, as a batch, etc.), a single geographic region(e.g., requested location), and/or any other suitable location(s). Allor portions of the method can be repeated for different geographicregions, timeframes, and/or otherwise repeated.

Training a representation model S100 functions to train a model to beagnostic to common changes at a geographic region. The representationmodel can be trained one time, periodically (e.g., daily, weekly,monthly, yearly, etc.), at random times, responsive to a request,responsive to receipt of additional information, and/or any othersuitable time frequency. The representation model can be specific to aproperty (e.g., a property identifier, a parcel identifier, etc.), to aproperty class (e.g., residential properties, commercial properties,etc.), a geographic region (e.g., by street, by town, by city, bycounty, by state, by country, a geofence of any geographic region in theworld, a geographic identifier, etc.), a settlement class (e.g., urban,suburban, rural, etc.), a rare change type (e.g., object removed, objectadded, object modified, hail, fire, earthquake, flood, etc.), a seasontype (e.g., autumn, winter, spring, summer, etc.), a climate type (e.g.,tropical rainy, dry, temperate marine, temperate continental, polar,highlands, etc.), and/or be otherwise specific. Additionally oralternatively, the model can be generic across properties, propertyclasses, geographic regions, settlement classes, rare change types,season types, climate types, and/or be otherwise generic.

As shown in FIG. 2 and FIG. 6 , S100 can include: determining arepresentation for a geographic region S110 and training therepresentation model based on the representation S120.

Determining a representation for a geographic region S110 functions todetermine a representation for a geographic region using arepresentation model based on one or more measurements. S110 can beiteratively performed for each of a set of geographic regions (e.g.,training geographic regions), for each of a set of geographic regionmeasurements (e.g., corresponding to a training geographic region), foreach of a set of geographic region measurement pairs (e.g.,corresponding to a training geographic region), and/or any number oftimes.

S100 can include: determining a set of measurements for a geographicregion, and determining one or more representations of the geographicregion from the set of measurements. However, S110 can be otherwiseperformed.

The measurements used to determine one or more representations for thegeographic region can be a single measurement of the geographic region;a pair of measurements (e.g., within a set or batch of measurementpairs); one or more batches of measurements (e.g., a first batch and asecond batch, wherein the first and second batches can include the sameor different measurements of the same geographic region); and/or beotherwise defined. The measurements are preferably heterogeneousmeasurements (e.g., differing in one or more features, differingmeasurement contexts, etc.), but can alternatively not be heterogeneousmeasurements (e.g., be homogeneous).

The measurements (e.g., a first and second measurement) preferablydiffer in common changes, but can additionally or alternatively notdiffer in common changes. The measurements can be specific to a changetype (e.g., a common change type; such as the first measurement includesa car, but the second measurement does not include a car, etc.) and/ornot be specific to a change type (e.g., the first measurement includes acar, second measurement includes clouds, etc.). The measurements arepreferably an unlabeled set (e.g., pair) of measurements, wherein one ormore change information elements is unknown for the set of measurements.For example, unlabeled change information can include: rare changeoccurrence (e.g., whether the pair of measurements depicts a rarechange), common change occurrence (e.g., whether the pair ofmeasurements depicts a common change), change type (e.g., rare changetype, common change type, etc.), change extent, change magnitude, changetime, change cost, change description, and/or any other changeinformation.

The measurements are preferably associated with different measurementtimes (e.g., sampled at different times), but can additionally oralternatively be associated with the same time. For example, a firstmeasurement is associated with a first time and a second measurement isassociated with a second time. In examples, the difference between thefirst and second time can be 6 hours, 12 hours, 1 day, 1 week, 2 weeks,1 month, 2 months, 4 months, 6 months, 1 year, 2 years, 5 years, 10years, and/or any other time difference. The difference between thefirst and second time can optionally be greater than a threshold timedifference, wherein the threshold time difference can be between 1 day-5years or any range or value therebetween (e.g., 1 day, 1 week, 2 weeks,1 month, 6 months, 1 year, 2 years, 5 years, etc.), but canalternatively be less than 1 day or greater than 1 year.

The measurements preferably represent (e.g., depict) the same geographicregion, but can additionally or alternatively represent differentgeographic regions. For example, a first measurement depicts a firstproperty within the geographic region (e.g., neighborhood) and a secondmeasurement depicts a second property within the geographic region.

A measurement can optionally be determined based on another measurement.For example, a second measurement can be determined based on a firstmeasurement (e.g., to simulate a common and/or rare change, to simulateadditional measurement variability, to increase robustness of therepresentation model, to adjust a distribution of the training data, todebias the training data, etc.). In examples, the second measurement canbe a modified first measurement (e.g., modified using a model). Examplesof modifications include: color adjustments, measurement pose (e.g.,position and/or angle) adjustments, resolution adjustments, addition ofobstructions (e.g., trees), removal of obstructions, and/or any othermodifications.

The measurements can be randomly selected (e.g., from a set ofmeasurements for a geographic region), selected based on a distributionof common changes, selected based on measurement time, sequentiallyselected (e.g., iterate through each of a set of measurements to performS110 for each pair), include all available measurements for a geographicregion, exclude measurements associated with known rare changes (e.g.,disasters, remodeling, etc.), and/or otherwise selected.

The measurements can: include only measurements depicting commonchanges, exclude measurements depicting rare changes, include bothmeasurements including both common changes and rare changes (e.g., aspositive and negative test sets, respectively), include a set ofmeasurements with unknown common/rare change type (e.g., unlabeled data,such as for self-supervised feature learning), and/or include any othersuitable measurements.

Determining one or more representations of the geographic region fromthe set of measurements can include: extracting a representation of thegeographic region from a single measurement, from multiple measurements,and/or from any other suitable set of measurements. The representationpreferably includes a set of features (e.g., nonsemantic features,semantic features, etc.), but can additionally or alternatively includea set of labels and/or any other suitable representation. Therepresentation is preferably determined by the representation model(e.g., trained or untrained), but can alternatively be determined by thechange classification model and/or by any other suitable model.

In a first variant, S110 includes determining a geographic regionrepresentation based on a single measurement. For example, S110 caninclude determining a first and second representation for a geographicregion, based on a first and second measurement, respectively. The firstrepresentation and the second representation are preferably extracted bythe same representation model, but can additionally or alternatively beextracted by different representation models. Examples of differentrepresentation models can include: an online model and target model pair(example shown in FIG. 5A and FIG. 5B), related models (e.g., whereinthe models share all or some of the weights; wherein the weights for onemodel are derived from the weights of the other), models with differentbase networks, and/or other representation models. In an example usingthe same representation model, the model input for a representationmodel (e.g., neural network) is a first measurement (e.g., first image),and the model output for the representation model (e.g., neural network)is a first representation (e.g., first vector). The model input for thesame representation model (e.g., neural network) is a second measurement(e.g., second image), and the model output for the same representationmodel (e.g., neural network) is a second representation (e.g., secondvector); examples shown in FIG. 3A and FIG. 3B.

In a second variant, S110 includes determining a geographic regionrepresentation based on multiple measurements (e.g., the representationmodel receives two or more measurements and outputs a singlerepresentation). In a specific example, the representation can be ameasurement-pair representation, wherein the representation is for apair of measurements. In a first embodiment, the geographic regionrepresentation (e.g., the measurement-pair representation) can be acomparison metric (e.g., determined via S120 methods) between a firstintermediate representation and a second intermediate representation(e.g., wherein the intermediate representations are determined using thefirst variant). In a second embodiment, the geographic regionrepresentation (e.g., the measurement-pair representation) can be aconcatenated first intermediate representation and second intermediaterepresentation. In a third embodiment, the geographic regionrepresentation can be an aggregated representation for a set ofintermediate representations (e.g., wherein each of the intermediaterepresentations is determined based on a single measurement). Forexample, an intermediate representation can be determined for eachproperty in a geographic region (e.g., determined based on acorresponding measurement for the property), wherein the aggregatedrepresentation is concatenation, average (e.g., weighted average),and/or any other aggregation of the set of intermediate representations(e.g., example shown in FIG. 20 ). In a fourth embodiment, thegeographic region representation (e.g., the measurement-pairrepresentation) can be directly determined based on multiplemeasurements (e.g., without intermediate representations). In a fourthembodiment, the representation can be a representation of theconcatenated representations. The multiple measurements can be twomeasurements (e.g., a first and second measurement, as described in thefirst variant) and/or any number of measurements.

However, the representation for a geographic region can be otherwisedetermined.

Training the representation model based on the representation S120functions to train a representation model using the representation asinput. The representation model can be used to determine a baselinerepresentation for a geographic region, be used to determine whether achange has occurred, and/or be otherwise used. The model is preferablythe same representation model from S110, but can additionally oralternatively be a different model. S120 can be performed after S110(e.g., after each instance of S110, after S110 is iteratively performedfor each of a set of training geographic regions, etc.), and/or at anyother time. S120 can be performed once (e.g., for each geographicregion, for each pair of first and second measurements, etc.),iteratively until a stop condition is met, and/or at any other time. Inexamples, the stop condition can include: a comparison metric indicatesthat a first and second representation (determined via S110) for a setof geographic regions are substantially similar (e.g., on average), whena predetermined number of representation pairs or batches (correspondingto measurement pairs or batches) have been processed, when apredetermined number of training iterations have been performed, and/orany other stop condition.

The representation model can be trained using self-supervised learning(e.g., noncontrastive self-supervised learning; contrastiveself-supervised learning; etc.), but additionally or alternatively betrained using semi-supervised learning, supervised learning,unsupervised learning, transfer learning, reinforcement learning,single-shot learning, zero-shot learning, and/or any other suitabletraining method. In an example, the representation model can be trainedusing noncontrastive self-supervised learning, wherein the measurementsare assumed to be positive examples (e.g., depict common changes only).In a first specific example, the training measurements only includemeasurements depicting common changes. In a second specific example, thetraining measurements can include measurements depicting rare and commonchanges (e.g., wherein the rare changes are rare enough that they do notsubstantively affect the overall model training). Examples ofself-supervised learning that can be used can include: bootstrap yourown latent (BYOL), bidirectional encoder representations fromtransformers (BERT), momentum contrast (MoCo), contrastive learning ofvisual representations (SimCLR), instance discrimination, contrastivepredictive coding (CPC), Deep InfoMax, and/or any other suitableself-supervised learning method.

In a first variant, training the representation model includesdetermining a comparison metric (e.g., based on a first and secondrepresentation of the same geographic region), and training therepresentation model using the comparison metric; examples shown in FIG.4A and FIG. 4C. The comparison metric can be discrete, continuous (e.g.,distance, probability of a rare change occurrence, etc.), binary (e.g.,rare change occurrence, no rare change occurrence), multiclass, and/orotherwise structured. The comparison metric can include: a loss functionmetric, a distance metric, a similarity metric (e.g., Jaccardsimilarity, cosine similarity, etc.), a dissimilarity metric, aclustering metric, a threshold comparison, a statistical measure, and/orany other suitable metric. Distance metrics can include a vectordistance (e.g., a distance between a first representation vector and asecond representation vector), wherein the distance is determined using:cosine distance, Euclidean distance, Chebyshev distance, Mahalanobisdistance, and/or any other method.

In a first embodiment of the first variant, the comparison metric isbased on a comparison between a first and second geographic regionrepresentation (e.g., determined via S110 methods). For example, thecomparison metric can represent a similarity between the firstrepresentation and the second representation. In a specific example, thecomparison metric can be a vector distance between a first vectorrepresentation and a second vector representation. In a secondembodiment of the first variant, the comparison metric is based on acomparison between a representation and an aggregated representation. Inan illustrative example, the comparison metric is based on a vectordistance between a representation for a property of interest and anaggregate representation for other properties within a geographicregion. In a third embodiment of the first variant, the comparisonmetric is based on a single geographic region representation (e.g., aconcatenated representation, an aggregate representation, etc.).

In variants, the representation model can be updated (e.g., therepresentation model weights are updated) when the comparison metricdoes not satisfy a training target. In a first variant, the trainingtarget includes the comparison metric equal to 0 (e.g., substantiallyequal to 0). For example, training the representation model can includeminimizing the absolute value of the comparison metric. In a specificexample, training the representation model can include training therepresentation model to predict the same first and secondrepresentations for the geographic region (e.g., based on differentmeasurements of the geographic region). In a second variant, thetraining target includes the comparison metric (and/or an absolute valueof the comparison metric) is less than a threshold (e.g., a trainingthreshold). However, any other training target can be used to train therepresentation model.

In a second variant, the representation model can be trained using BYOL;example shown in FIG. 5A. The first measurement and the secondmeasurement are ingested by an online network and a target network,respectively. The online network is trained to predict the targetnetwork representation of the second measurement, and the target networkis updated with a slow-moving average of the online network. The onlinenetwork is preferably used for inference in S330, but the target networkcan alternatively be used.

However, the representation model can be otherwise trained.

Training a change classification model S200 functions to train thechange classification model to classify the type of change (e.g., thetype of rare change) depicted across pre-change and post-changemeasurements. S200 can be performed after S100, during S100, and/or atany other suitable time.

The change classification model can be trained using supervisedlearning, but additionally or alternatively be trained usingsemi-supervised learning, self-supervised learning, unsupervisedlearning, transfer learning, reinforcement learning, single-shotlearning, zero-shot learning, and/or any other suitable method.

Training a change classification model to determine a change type (e.g.,rare change type) preferably uses ground-truth data (e.g., with labelsof change type), but can alternatively not use ground-truth data. Thechange classification model can be trained using a set of individualmeasurements labeled with a change type (and/or change description), aset of measurement pairs (e.g., pre- and post-change) labeled with anintervening change type (and/or change description), a set of individuallabeled representations (e.g., individual representations, individualmeasurement-pair representations, etc.), a set of labeled representationpairs, and/or any other suitable set of training data. In example,change type labels and/or change description labels can be determined:manually, automatically (e.g., by comparing text descriptions of thefirst and second measurements), using predetermined assignments, and/orotherwise determined. The change classification model can be trained topredict the ground-truth labels, and/or predict any other suitableoutput.

Examples of rare change types can include: addition, removal, ormodification of an object (e.g., tree, structure, building, pool, deck,road, roof, structure, water level, etc.); remodeling or repair work(e.g., construction work, reparation work, ground remodeling,repainting, reroofing, etc.); damage; demolition; and/or any othersuitable change type. Specific examples of rare change types can includechanges in: physical geometry of permanent structures (e.g., more than athreshold amount of geometric change in an object associated with apermanent label); property condition (e.g., displacement of shingles ona roof, addition and/or removal of debris, garden remodeling, etc.),condition of surrounding trees (e.g., broken tree limbs, torn bark,shredded foliage, etc.), water level of body of water (e.g., increasingwater level of lake), and/or any other suitable change.

In a first variant, the change classification model is therepresentation model (e.g., trained via S100), wherein therepresentation model can be trained to output a rare change label (e.g.,change type and/or change description) by labeling the training data(e.g., the same training data used in S100) with the rare change type;example shown in FIG. 7 . For example, the representation model can betrained to predict the representation and the rare change type.

In a second variant, the change classification model is therepresentation model (e.g., trained via S100), wherein therepresentation model can be tuned to be specific to a rare change type(e.g., after the representation model is trained to output the samerepresentation given different measurements of the same geographicregion); example shown in FIG. 9 . The representation model can be tunedto predict the representation and the rare change type, tuned to beagnostic to other rare change types (e.g., wherein other rare changesare included in the training set for initial representation modeltraining in S100), and/or otherwise tuned. The representation model canbe tuned using pre-change and post-change measurement pairs (e.g., imagepairs) for the same geographic region for a given rare change type,and/or using any other suitable training data.

In a third variant, the change classification model is different fromthe representation model, wherein the change classification model istrained to predict the change type; example shown in FIG. 8 . In a firstexample, a first representation can be extracted from a pre-rare changemeasurement and a second representation can be extracted from apost-rare change measurement (e.g., using the representation model,wherein the change classification model is downstream of therepresentation model). The first and second vectors can be ingested bythe change classification model, and the change classification model canbe trained to predict the rare change type. In a second example, apre-rare change measurement and a post-rare change measurement can beingested by the change classification model, and the changeclassification model can be trained to predict the rare change type.

In a first embodiment, the change classification model includes aclassifier (e.g., linear classifier) and/or a jointly trained imagefeature extractor that predicts the change type label. In this example,the rare change type labels can be multiclass, including one of apredetermined set of rare change types (e.g., x added, x removed, xremodeled, etc.).

In a second embodiment, the change classification model includes and/orinterfaces with a text encoder and/or an image encoder (e.g., the changeclassification model is a CLIP model). The text encoder input caninclude a set of change descriptions and/or any other suitable inputs.The text encoder output can include a set of text representations (e.g.,vector, embedding, array, set, matrix, encoding, multidimensionalsurface, single value, etc.), each text representation corresponding toa change description in the set of change descriptions. The imageencoder can be a representation model (e.g., the same representationmodel trained in S100, a different representation model, etc.) and/orany other image encoder. The image encoder input can includemeasurements (e.g., a pair of measurements) and/or any other suitableinputs. The image encoder output can include a measurement-pairrepresentation and/or any other suitable output.

At test time, the change classification model can determine the changetype (e.g., a change description corresponding to a change type) basedon text representation(s) (e.g., output from the text encoder) andmeasurement-pair representation(s) (e.g., output from the imageencoder). For example, the change classification model can determine alikelihood that measurements (e.g., a first measurement and a secondmeasurement) depict a given change type based on geographic regionrepresentations (e.g., a first representation and a secondrepresentation, a measurement-pair representation, etc.) and a textrepresentation (e.g., corresponding to a change description for thegiven change type). In a specific example, the trained text encodersynthesizes a zero-shot classifier (e.g., linear classifier) byembedding target change descriptions, and predicts the text describingthe rare change occurring between two measurement inputs. In an example,the change classification model determines a comparison metric based onthe text representation and the measurement-pair representation, whereinthe comparison metric represents a likelihood that the measurements usedto determine the measurement-pair representation depict the changedescription used to determine the text representation. The comparisonmetric can be the same comparison metric used in S100 or a differentcomparison metric. In a first specific example, the changeclassification model outputs the comparison metric. In a second specificexample, when the comparison metric is above a threshold (e.g., above apredetermined match probability, above a second-highest matchprobability, etc.), the change classification model outputs the changedescription (e.g., indicating the measurements depict a changecorresponding to the change description). In a third specific example,the change classification model selects one or more change descriptionsfrom a set of change descriptions (e.g., each with a corresponding textrepresentation and comparison metric) associated with the lowestcomparison metrics (e.g., the most likely applicable changedescription). However, the change classification model can otherwise beconfigured.

In a first example of training the change classification model, theimage encoder and text encoder are trained together (e.g.,simultaneously, concurrently, using the same training data set, etc.).In this example, the image encoder is preferably not the representationmodel trained in S100 (e.g., wherein the image encoder is trained usingsupervised learning and the representation model from S100 is trainedusing self-supervised learning), but can alternatively be the samerepresentation model. For example, the text encoder and image encodercan be trained using CLIP methods (e.g., modified CLIP methods) with alabeled training dataset (e.g., ground truth data) to predictchange-text pairs (e.g., a measurement-pair representation and a textrepresentation corresponding a measurement pair input and a changedescription, respectively). The labeled training dataset can includepairs of measurement pairs and change descriptions (e.g., twomeasurements and one or more change descriptions in each training pair)and/or pairs of any number of measurements and change descriptions.Examples are shown in FIG. 16A and FIG. 17A.

In a second example of training the change classification model, theimage encoder and text encoder are trained separately (e.g., usingdifferent training data sets, etc.). In this example, the image encoderis preferably the representation model trained in S100, but canalternatively not be the same representation model. For example, theimage encoder can be trained via S100 methods, and the text encoder canbe trained using CLIP methods (e.g., modified CLIP methods) with alabeled training dataset (e.g., ground truth data) to predictchange-text pairs. The labeled training dataset can include pairs ofmeasurement-pair representations and change descriptions (e.g., onerepresentation and one or more change description in each trainingpair), pairs of geographical region representation pairs and changedescriptions (e.g., two representations and one or more changedescription in each training pair), and/or pairs of any number ofrepresentations and change descriptions. Examples are shown in FIG. 16Band FIG. 17B.

However, the change classification model can be otherwise trained.

Evaluating a geographic region S300 functions to detect a rare change toa geographic region (e.g., a rare change occurrence) using the trainedmodel. S300 can be performed after S100, after S200, and/or at any othertime. In variants, S300 can be performed for each of a set of geographicregions (e.g., for each of a set of properties within a region).

As shown in FIG. 2 and FIG. 6 , S300 can include: determining a baselinerepresentation for the geographic region S310, determining a testrepresentation for the geographic region S320, detecting a change S330,optionally characterizing the change S340, optionally determining achange time, optionally providing an output, and/or any other suitableelement.

Determining a baseline representation for the geographic region S310functions to determine a first comparison point for the geographicregion (e.g., the property). S310 is preferably performed before S320and/or contemporaneously with S320 (e.g., the baseline measurement isselected during S320), but alternatively can be performed after S320and/or any other suitable time.

In a first variant, the baseline representation can be determined basedon a baseline measurement for the geographic region using the trainedrepresentation model (e.g., extracted from the baseline measurementusing the trained representation model). The trained representationmodel is preferably the representation model trained in S100, but canalternatively be a different model. The baseline measurement ispreferably sampled before occurrence of a rare change, but canalternatively be sampled at any other suitable time. The baselinemeasurement can be selected randomly, according to a set of heuristics(e.g., selecting the earliest measurement associated with the geographicregion, selecting the most recent measurement associated with thegeographic region prior to a hazard event, etc.), the measurementsampled at a time closest to a baseline time (e.g., a user inputbaseline time), and/or otherwise selected from a set of measurements forthe geographic region of interest.

In a first example, S310 can be pre-performed for each of a set ofgeographic regions, wherein the baseline representations can be storedin association with a geographic region identifier and subsequentlyretrieved for test representation evaluation. In variants, the baselinemeasurement can be discarded after baseline representation extraction,which can reduce the amount of data that needs to be stored for a givengeographic region (e.g., property). In a second example, S310 isperformed with S320, wherein both the baseline representation and thetest representation are contemporaneously selected from the measurementsfor the geographic region of interest.

In a second variant, the baseline representation can be retrieved from adatabase, wherein the baseline representation for the geographic regionwas previously determined as described in the first variant.

However, the baseline representation for the geographic region can beotherwise determined.

Determining a test representation for the geographic region S320functions to determine a second comparison point for the geographicregion. S320 can be performed after a known rare change event (e.g.,after a hailstorm, after remodeling the property, etc.), periodically,for each new measurement of the geographic region, in response to arequest, and/or any other suitable time. S320 is preferably performedafter S310 and/or contemporaneously with S310 (e.g., the baselinemeasurement is selected during S320), but alternatively can be performedbefore S310 and/or any other suitable time.

In variants, the test representation can be determined based on a testmeasurement for the geographic region using a trained representationmodel. The trained representation model is preferably the samerepresentation model used in S310, but can alternatively be a differentmodel. The test measurement is preferably sampled after a rare change,but can alternatively be sampled at any other suitable time. The testmeasurement is preferably the most recent measurement for the geographicregion, but can alternatively be a past measurement for the geographicregion, a randomly selected measurement, a measurement selectedaccording to a set of heuristics (e.g., the most recent measurementafter a hazard event), the measurement sampled at a time closest to atest time (e.g., a user input test time), and/or any other suitablemeasurement.

The baseline measurement and test measurement are preferablyheterogeneous measurements (e.g., differing in one or more features,differing measurement contexts, with one or more common changes betweenthe measurements, etc.), but can alternatively not be heterogeneousmeasurements.

The baseline measurement and test measurement are preferably associatedwith different measurement times (e.g., sampled at different times), butcan additionally or alternatively be associated with the same time. Thetest measurement is preferably sampled after the baseline measurement,but can alternatively be sampled before the baseline measurement,concurrently with the baseline measurement, and/or any other suitabletime compared to the sampling of the baseline measurement. For example,the baseline measurement is associated with a first time and the testmeasurement is associated with a second time. In examples, thedifference between the first and second time can be 6 hours, 12 hours, 1day, 1 week, 2 weeks, 1 month, 2 months, 4 months, 6 months, 1 year, 2years, 5 years, 10 years, and/or any other time difference. Thedifference between the first and second time can optionally be greaterthan a threshold time difference, wherein the threshold time differencecan be between 1 day-5 years or any range or value therebetween (e.g., 1day, 1 week, 2 weeks, 1 month, 6 months, 1 year, 2 years, 5 years,etc.), but can alternatively be less than 1 day or greater than 1 year.

The baseline measurement and test measurement preferably represent(e.g., depict) the same geographic region, but can additionally oralternatively represent different geographic regions. The testmeasurement preferably encompasses substantially the same geographicextent as the baseline image, but can alternatively encompass more orless of the graphic extent as the baseline image.

However, the test representation for the geographic region can beotherwise determined.

Detecting a change S330 functions to detect occurrence of a rare changeby comparing the baseline representation and the test representation.S330 is preferably performed after S310 and S320, but can alternativelybe performed at any other time.

In a first variant, the rare change can be detected by determining acomparison metric between the baseline representation (e.g., vector) andthe test representation (e.g., vector) extracted from the baselinemeasurement (e.g., image) and the test measurement (e.g., image) of thesame geographic region by the trained representation model (e.g.,examples shown in FIG. 4B and FIG. 4D). The comparison metric can be thesame comparison metric used in S100 or a different comparison metric.The detection of the rare change occurrence can be the comparison metricand/or be based on the comparison metric. For example, since the trainedrepresentation model is trained to be agnostic to common changes (e.g.,the trained representation model would output substantially the samerepresentation for the same geographic region if no rare changes hadbeen detected), a rare change can be detected when the baselinerepresentation and the test representation do not match and/or differbeyond a threshold difference (e.g., a rare change threshold). A rarechange can be detected when the comparison metric (e.g., absolute valueof the comparison metric) is greater than a threshold (e.g., the samethreshold used as the representation model training target and/or adifferent threshold). The threshold can be 0 and/or a value greater than0 (e.g., 0.1, 0.2, 0.25, 0.5, 0.75, 1, 5, 10, 100, etc.). In anillustrative example, the comparison metric can be a cosine similarity(e.g., a number between 0 and 1) that measures the similarity betweenthe baseline region representation and the test region representationthat are represented as non-zero vectors. The rare change is detected ifthe cosine similarity is greater than the threshold (e.g., cosinesimilarity=0.8, threshold=0.5, 0.8>0.5). Otherwise, the rare change isnot detected.

In a second variant, the rare change can be detected using a classifier.For example, the classifier can ingest the baseline representation andthe test representation, and output a classification of “change” or “nochange”.

However, the change can be otherwise detected.

The method can optionally include characterizing the change S340, whichfunctions to determine change information. Characterizing the change ispreferably performed after the change has been detected in S330, but canalternatively be performed with change evaluation and/or at any othersuitable time.

Characterizing the change can include determining a change type, achange description, a change extent, a change magnitude, a changeuncertainty, and/or any other suitable change information.

In a first variant, characterizing the change can include determining achange type.

In a first example, the change type can be determined by using a changeclassification model (e.g., trained in S200). The change classificationmodel ingests: the baseline representation and the test representation(e.g., separately or in a concatenated format), a comparison metricbased on the baseline and test representations, the baseline measurementand the test measurement, and/or any other suitable inputs. The changeclassification model outputs the change type, a confidence score, and/orany other suitable information.

In a second example, the change type can be determined by using a changeclassification model (e.g., trained CLIP model) that ingests a baselinemeasurement or representation and a test measurement or representation(e.g., separately or in a concatenated format) and outputs a changedescription. The resultant change description can optionally besearchable in a database; example shown in FIG. 18 .

In a third example, the change type can be determined by using a changeclassification model (e.g., trained CLIP model). In a first specificexample, the change classification model ingests the baselinemeasurement, the test measurement, and a set of change descriptions, andoutputs a measurement-pair representation and one or more textrepresentations (e.g., wherein each text representation corresponds to achange description of the set of change descriptions); example shown inFIG. 19A. In a second specific example, the change classification modelingests a measurement-pair representation and/or a set of changedescriptions, and the change classification model outputs one or moretext representations (e.g., wherein each text representation correspondsto a change description of the set of change descriptions); exampleshown in FIG. 19B. The set of change descriptions is preferablydetermined by a user (e.g., includes the set of change descriptions thatthe user is looking for), but can additionally and/or alternatively bedetermined automatically, and/or otherwise determined. The set of changedescriptions can be different from the change descriptions used in thetraining data to train the change classification model (e.g., S200), orcan be the same change descriptions used in the training data to trainthe model. In a specific example, the change classification model can:determine (test) text representations of the set of (test) changedescriptions, generate a measurement-pair representation (e.g., based onthe test images), and/or generate a matrix (e.g., of size 1 by number ofchange descriptions within the set) relating the measurement-pairrepresentation and the set of text representations (“change-text pair”).A comparison metric can be determined for each change-text pair, whereinthe change description associated with the change-text pair having thebest comparison metric (e.g., lowest value, highest value, etc.) isselected as the change type.

In a second variant, characterizing the change can include determining achange magnitude (e.g., scoring the change magnitude, binning the changemagnitude, etc.). In a first example, the change magnitude can bedetermined by another trained model, given the baseline and testrepresentations. In a second example, the change magnitude can bedetermined based on a comparison metric between the baselinerepresentation and the test representation. The comparison metric can bethe same comparison metric used in S100, the same comparison metric usedin S200, the same comparison metric used in S3300, and/or a differentcomparison metric. The change magnitude can be the comparison metricand/or can be otherwise based on the comparison metric (e.g., determinedusing a model that outputs the magnitude based on the comparisonmetric).

A change uncertainty (e.g., uncertainty parameter, confidence interval,etc.) can optionally be determined based on the change classificationmodel (e.g., an output of the change classification model), based on thecomparison metric, and/or otherwise determined.

However, the change can be otherwise characterized.

The method can optionally include determining a change time, whichfunctions to estimate when a change occurred (e.g., determine thetimeframe in which the change occurred). Determining a change time ispreferably performed after the change has been detected in S330, but canalternatively be performed at any other suitable time. The change timecan be a time stamp (e.g., second, minute, hour, day, date, year,quarter, season, etc.), a time step, a time window, and/or any othersuitable temporal descriptor. In variants, the change time can bedetermined by searching through a timeseries of measurements and/orrepresentations therefrom for the geographic region to identify thepoint at which the representation changed. Examples of search algorithmsthat can be used include: binary search (e.g., example shown in FIG. 10; iteratively evaluating for changes using pairwise comparisons, etc.),linear search, hashing search, and/or any other suitable algorithm. Thesearch on the series of images can be performed backwards (e.g., from acurrent time), forwards (e.g., from a specified date of interest),and/or otherwise performed. The timeseries of measurements can bebounded by the baseline and test measurements, by randomly selectedmeasurements, include images proximal a known change event (e.g., aknown flood date, a known fire date, an insurance claim date, etc.),and/or include any other suitable set of images.

In a specific example, determining a change time includes identifying aseries of measurements of the geographic region based on the respectivemeasurement times (e.g., a series of measurements sorted by measurementtime), setting the baseline measurement as the first measurement of theseries of measurements, and setting the test measurement as the lastmeasurement of the series of measurements. The change time can bedetermined by performing a binary search on the series of measurements(e.g., performing a binary search on a sorted list), repeating all orparts of S300 for every comparison between two measurements within theseries of images, and/or otherwise determined. In an illustrativeexample, performing a binary search can include iteratively bisectingthe series of measurements and repeating all or parts of S300 for thebisected series (e.g., for the baseline measurement and a measurementsubstantially halfway between the baseline and test measurements; forthe test measurement and a measurement substantially halfway between thebaseline and test measurements; etc.), thus iteratively narrowing thechange time range (e.g., until the rare change is detected between twoadjacent measurements).

However, the change time can be otherwise determined.

Providing the change functions to provide one or more change informationelements (e.g., change type, change extent, change magnitude, changetime, change cost, change occurrence, change description, changeuncertainty, etc.) to an endpoint (e.g., physically an endpoint on anetwork, customer endpoint, user endpoint, automated valuation modelsystem, etc.) through an interface. The interface can be a mobileapplication, web application, desktop application, an API, and/or anyother suitable interface executing on a user device, gateway, and/or anyother computing system. Providing the change can be based on thegeographic region, based on the user request, and/or otherwise based.

Providing the change can additionally or alternatively include proving acomparison metric, a baseline representation or measurement, a testrepresentation or measurement, a heat map, and/or any other suitableattribute describing the change.

In a first variant, providing the change can include presenting a visualdisplay (e.g., numerical values, baseline measurement, test measurement,a heat map, etc.) to the user on a web application (e.g., native,browser, etc.). In a specific example, a change density map can beprovided, wherein the change density is based on change information foreach of a set of geographic regions (e.g., wherein a higher changedensity is associated with a higher regional density of changeoccurrence and/or a higher change magnitude). In a second variant,providing the change can include providing a text response (e.g., an APIresponse, such as JSON, CSV, etc.).

However, the change can be otherwise provided.

However, the geographic region can be otherwise evaluated.

Determining a geographic region satisfying a change criterion S4O0functions to identify one or more geographic regions associated with oneor more elements of change information (e.g., a change type, changedescription, change time, change extent, etc.). S400 can be performedafter S300 and/or at any other time. For example, S300 can beiteratively performed for each of a set of geographic regions, whereinS400 includes selecting a subset of the set of geographic regions basedon the evaluation of each of the set of geographic regions.

Determining the geographic region can include selecting a subset ofgeographic regions from a set of geographic regions that satisfy one ormore criteria. The set of geographic regions can include all geographicregions (e.g., in the database), a predetermined set of geographicregions, a manually defined set of geographic regions, geographicregions associated with one or more geographic region information (e.g.,location, property class, etc.), geographic regions associated with arare change (e.g., all geographic regions that have experienced a rarechange), and/or otherwise determined. The subset of geographic regionscan include all geographic regions that satisfy the criteria, apredetermined number of geographic regions (e.g., the 10 geographicregions that best satisfy the criteria), a predetermined percentage ofthe set of geographic regions, and/or any number of geographic regions.The criteria can be predetermined, manually determined, determined basedon a user input (e.g., a query), and/or otherwise determined.

In a first variant, the criteria include detecting a rare change for thegeographic region. In a second variant, the criteria include a changetime associated with the geographic region matching a criteria changetime (e.g., a specific time and/or a time range). In a third variant,the criteria include a change type associated with the geographic regionmatching a criteria change type and/or change description. In anyvariant, matching can include an exact match, a match within a thresholdvalue (e.g., wherein the threshold value can be based on a changeuncertainty parameter), and/or any other comparison.

S400 can optionally include providing the geographic region to anendpoint, which can function to provide one or more geographic regionsto an endpoint through an interface. Providing the geographic region canbe based on change information, based on a user request, and/or based onany other suitable input. However, the geographic region can beotherwise provided.

However, the geographic region can be otherwise determined.

6. Use Cases.

All or portions of the methods described above can be used for automatedproperty valuation, for insurance purposes, and/or otherwise used. Forexample, any of the outputs discussed above (e.g., for the geographicregion) can be provided to an automated valuation model (AVM), which canpredict a property value based on one or more of the attribute values(e.g., feature values), generated by the one or more models discussedabove, and/or attribute value-associated information. The AVM can be:retrieved from a database, determined dynamically, and/or otherwisedetermined.

In variants, the rare changes can be used to determine: automatedvaluation model error, automated valuation model accuracy, automatedproperty valuation or price, and/or any other suitable value. The rarechanges can be used with: real estate property investing (e.g., identifyunderpriced properties that can increase in value through renovationand/or repairs; incorporate the rare change into a valuation model toestablish the offer price; determine when construction, remodeling,and/or repairing has occurred; identify properties in portfolio thathave suffered damage; etc.), real estate management (e.g., identifyareas that can be renovated, repaired, added, and/or removed, etc.),real estate valuations (e.g., use rare change as an input to anautomated valuation model; use rare change to detect error in propertyevaluation models; use rare change as a supplement to a property-levelvaluation report; etc.), real estate and loan trading (e.g., detectillegal builds; identify deterioration since prior due diligence wascompleted; incorporate the rare change into collateral valuation inmortgage origination and in secondary mortgage market; etc.), insuranceunderwriting (e.g., determine pricing of insurance depending on the rarechange; optimize inspection to identify where to send inspectors;determine when to reach out to adjust insurance policy when remodelingis detected; identify which properties to initiate claims for; createopportunities to proactively address rare change issues before theyresult in insurance claims or other rare change-related losses; etc.);for customer target and/or acquisition (e.g., identify geographicregions where a product, such solar panels, has been widely adopted andtarget the remaining customers who haven't purchased the productresiding in those geographic regions); for municipality management(e.g., identify unpermitted modifications of a property); identify whichproperties and/or geographic regions to run further analyses on;, suchas post-disaster analysis (e.g., search through only geographic regionswhere a rare change has been detected, determine a weather event impacton a geographic region, etc.), change identification (e.g., determineall geographic regions where a specific change has occurred, determine achange density map by analyzing each of a set of geographic regions,etc.), timeseries analysis (e.g., monitor a geographic region over timebased on rare change type and/or magnitude of change; identifygentrification of a neighborhood; etc.), typicality analysis, and/orother analyses; and/or otherwise used.

In a first example, the method can be used to determine a weatherevent's impact on a region (e.g., a neighborhood, census block group,zip code, any other geographic region, etc.). In a first specificexample, when rare changes are detected (e.g., with a change timesubstantially near a weather event time) for properties within theregion (e.g., greater than a threshold percentage of properties withinthe region), a weather event impact can be determined based on thenumber and/or proportion of properties with a rare change, the changeextent, the change magnitude, and/or any other change information foreach of the properties. In a second specific example, when a rare changeis detected for the region (e.g., based on aggregate representationsacross properties in the region, based on a representation for theregion as a whole, etc.), a weather event impact can be determined basedon the change occurrence, change extent, the change magnitude, and/orany other change information for the region.

In a second example, the method can be used to determine the typicality(e.g., similarity) for a geographic region of interest (e.g., exampleshown in FIG. 21 ). For example, the typicality can be determined basedon a comparison metric between a first representation for a geographicregion of interest determined using the trained representation model anda second representation (e.g., for a second geographic region, for anaggregate of a set of geographic regions such as neighboring geographicregions, etc.) determined using a trained representation model (e.g.,the same or different representation model). In an illustrative example,the distance between a representation for a property relative to therepresentation for the set of reference properties can be indicative ofthe property's typicality relative to the reference property set. Thereference property set can be: a neighborhood, market comparables, acensus block group, and/or other reference properties. Therepresentation for the reference property set can be aggregated fromrepresentations of individual properties within the reference propertyset (e.g., an average representation, median representation, etc.); begenerated based on a reference property measurement (e.g., image of theneighborhood); and/or otherwise generated.

However, all or portions of the method can be otherwise used.

The method can optionally include determining interpretability and/orexplainability of the trained model, wherein the identified featuresand/or attributes (and/or values thereof) can be provided to a user,used to identify errors in the data, used to identify ways of improvingthe model, and/or otherwise used. Interpretability and/or explainabilitymethods can include: local interpretable model-agnostic explanations(LIME), Shapley Additive explanations (SHAP), Ancors, DeepLift,Layer-Wise Relevance Propagation, contrastive explanations method (CEM),counterfactual explanation, Protodash, Permutation importance (PIMP),information-theoretic model interpretation such as Learning to Explain(L2X), partial dependence plots (PDPs), individual conditionalexpectation (ICE) plots, accumulated local effect (ALE) plots, LocalInterpretable Visual Explanations (LIVE), breakDown, ProfWeight,Supersparse Linear Integer Models (SLIM), generalized additive modelswith pairwise interactions (GA2Ms), Boolean Rule Column Generation,Generalized Linear Rule Models, Teaching Explanations for Decisions(TED), Class Activation Maps (CAM), and/or any other suitable methodand/or approach.

All or a portion of the models discussed above can be debiased (e.g., toprotect disadvantaged demographic segments against social bias, toensure fair allocation of resources, etc.), such as by adjusting thetraining data, adjusting the model itself, adjusting the trainingmethods, and/or otherwise debiased. Methods used to debias the trainingdata and/or model can include: disparate impact testing, datapre-processing techniques (e.g., suppression, massaging the dataset,apply different weights to instances of the dataset), adversarialdebiasing, Reject Option based Classification (ROC),Discrimination-Aware Ensemble (DAE), temporal modelling, continuousmeasurement, converging to an optimal fair allocation, feedback loops,strategic manipulation, regulating conditional probability distributionof disadvantaged sensitive attribute values, decreasing the probabilityof the favored sensitive attribute values, training a different modelfor every sensitive attribute value, and/or any other suitable methodand/or approach.

As used herein, “substantially” or other words of approximation can bewithin a predetermined error threshold or tolerance of a metric,component, or other reference, and/or be otherwise interpreted.

Different subsystems and/or modules discussed above can be operated andcontrolled by the same or different entities. In the latter variants,different subsystems can communicate via: APIs (e.g., using API requestsand responses, API keys, etc.), requests, and/or other communicationchannels.

Alternative embodiments implement the above methods and/or processingmodules in non-transitory computer-readable media, storingcomputer-readable instructions that, when executed by a processingsystem, cause the processing system to perform the method(s) discussedherein. The instructions can be executed by computer-executablecomponents integrated with the computer-readable medium and/orprocessing system. The computer-readable medium may include any suitablecomputer readable media such as RAMs, ROMs, flash memory, EEPROMs,optical devices (CD or DVD), hard drives, floppy drives, non-transitorycomputer readable media, or any suitable device. The computer-executablecomponent can include a computing system and/or processing system (e.g.,including one or more collocated or distributed, remote or localprocessors) connected to the non-transitory computer-readable medium,such as CPUs, GPUs, TPUS, microprocessors, or ASICs, but theinstructions can alternatively or additionally be executed by anysuitable dedicated hardware device.

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),contemporaneously (e.g., concurrently, in parallel, etc.), or in anyother suitable order by and/or using one or more instances of thesystems, elements, and/or entities described herein. Components and/orprocesses of the following system and/or method can be used with, inaddition to, in lieu of, or otherwise integrated with all or a portionof the systems and/or methods disclosed in the applications mentionedabove, each of which are incorporated in their entirety by thisreference.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

1. A method, comprising: determining a first measurement and a secondmeasurement of a geographic region; determining a first representationof the geographic region based on the first measurement using arepresentation model, wherein the representation model is trained to beagnostic to common changes; determining a second representation of thegeographic region using the representation model; and detecting a rarechange for the geographic region based on the first representation andthe second representation.
 2. The method of claim 1, wherein therepresentation model is trained to output substantially equivalenttraining representations based on different measurements of a traininggeographic region.
 3. The method of claim 1, further comprisingdetermining a distance between the first representation and the secondrepresentation, wherein the rare change is detected based on thedistance.
 4. The method of claim 1, wherein the representation modelcomprises an encoder trained using self-supervised learning.
 5. Themethod of claim 1, wherein the first measurement and the secondmeasurement correspond to a first time and a second time, respectively.6. The method of claim 1, wherein the first measurement and the secondmeasurement each comprise aerial images of the geographic regionacquired at a first angle and a second angle, respectively.
 7. Themethod of claim 1, further comprising classifying the rare change basedon the first representation and the second representation, using aclassification model.
 8. The method of claim 1, wherein therepresentation model is trained using self-supervised learning, themethod further comprising: determining a text representation based on adescription of a change type using a text encoder, wherein the textencoder is trained using supervised learning; and determining alikelihood that the first measurement and the second measurement depictthe change type based on the first representation, secondrepresentation, and the text representation.
 9. The method of claim 1,further comprising: determining a text representation based on adescription of a change type using a text encoder; determining ameasurement-pair representation based on the first measurement and thesecond measurement using an image encoder; and determining a likelihoodthat the first measurement and the second measurement depict the changetype based on the measurement-pair representation and the textrepresentation.
 10. A method, comprising: determining a first and asecond measurement of a geographic region; determining a firstrepresentation of the geographic region based on the first measurementusing a representation model, wherein the representation model istrained using self-supervised learning on a training dataset comprisingunlabeled measurements for a set of training geographic regions;determining a second representation of the geographic region based onthe second measurement using the representation model; determining acomparison metric based on the first representation and the secondrepresentation; and detecting a rare change for the geographic regionbased on the comparison metric.
 11. The method of claim 10, wherein therepresentation model comprises an encoder.
 12. The method of claim 10,wherein a percentage of the unlabeled measurements that depict rarechanges is less than a threshold.
 13. The method of claim 10, whereindetecting a rare change for the geographic region comprises detectingthat the comparison metric is greater than a threshold.
 14. The methodof claim 10, wherein, for each of the set of training geographicregions, the representation model is trained to output substantiallyequivalent training representations based on different measurements ofthe training geographic region.
 15. The method of claim 10, furthercomprising classifying the rare change based on the first representationand the second representation, using a classification model.
 16. Themethod of claim 10, wherein the unlabeled measurements within thetraining dataset depict common and rare changes.
 17. The method of claim10, wherein the first and a second measurements correspond to a firsttime and a second time, respectively, the method further comprisingrepeating the method using the first measurement and a thirdmeasurement, wherein the third measurement corresponds to a third timebetween the first time and the second time, wherein the change time isbased on at least one of the first or third times.
 18. The method ofclaim 10, wherein a rare change comprises at least one of propertydamage or property construction.
 19. The method of claim 10, wherein thegeographic region comprises a property parcel.
 20. The method of claim10, wherein the geographic region comprises a census block group.